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INFORMATION AND communication TECHNOLOGIES
CLASSIFICATION OF DIFFERENT FOREST TYPES WITH MACHINE
LEARNING ALGORITHMS
Kadir Sabanci1, M.Fahri Ünlerşen2, Kemal Polat3
1
Karamanoglu Mehmetbey University, Turkey
2
Necmettin Erbakan University, Turkey
3
Abant Izzet Baysal University, Turkey
[email protected]; [email protected]; [email protected]
Abstract
In this study, forest type mapping data set taken from UCI (University of California, Irvine) machine learning
repository database has been classified using different machine learning algorithms including Multilayer Perceptron,
k-NN, J48, Naïve Bayes, Bayes Net and KStar. In this dataset, there are 27 spectral values showing the type of three
different forests (Sugi, Hinoki, mixed broadleaf). As the performance measure criteria, the classification accuracy has
been used to evaluate the classifier algorithms and then to select the best method. The best classification rates have
been obtained 90.43% with MLP, and 89.1013% with k-NN classifier (for k=5). As can be seen from the obtained
results, the machine learning algorithms including MLP and k-NN classifier have obtained very promising results in
the classification of forest type with 27 spectral features.
Key words: Forest types, Multilayer perceptron, k-NN classifier, Data Mining.
Introduction
Today, as the number of measuring devices
increases, so does the number and types of data. As
a result of these advancements, it is required that so
much information is stored in databases, and that
this stored information is needed to be analyzed by
intelligent and automated processes which convert the
data into useful information and knowledge (Dener,
Dörterler, & Orman, 2009). Consequently, data
mining has become an important research area.
Data mining is a computational process that
reveals patterns in data sets by using such methods
like artificial intelligence, machine learning, statistics
etc (Chen, Han, & Yu, 1996). The methods used
in data mining are investigated in two groups as
predictive and descriptive. In predictive methods,
a model is created by using a dataset whose results
are known. For example in a bank, the properties of
customers who pay their credits back can be revealed,
and a model can be created by using previous data sets
about funding of them. Afterwards this model can be
used on new customers for determining the possibility
of paying their credits back. In descriptive methods,
a relationship can be searched between two data sets.
For example, the shopping haAabits of two different
cultures may be investigated for similarity (Özekes,
2003).
Data mining methods can be divided into three
groups due to their function.
• Classification and Regression
• Clustering
• Association Rules
In this study data mining methods are used
to classify the data set. In classification, training
examples are used to learn a model that can classify
the data samples into known classes. The classification
254
process involves following these steps: creating a
training data set, identifying class attributes and
classes, identifying useful attributes for classification,
relevance analysis, learning a model using training
examples in the training set and using the model to
classify the unknown data (Sharma & Jain, 2013). The
causes of selecting of machine learning algorithms in
data mining are that we can identify the tree types
automatically based on the spectral features of trees
and we can get very high identification success by
means of machine learning algorithms.
There are many studies in the literature in which
data mining classification algorithms are used. The
main areas are medical, food and agriculture. Jamuna
et al. (2010) used different classification algorithms
and compared these methods in order to ascertain the
productivity of cotton seed in the oncoming stages of
development. Although Decision Tree Classifier and
Multilayer Perceptron Methods produce results at
the same level of accuracy, it was observed that the
Decision Tree Classifier method produces results in
a much shorter time. Sabanci and Aydin (2014) used
image processing techniques to detect and spray weeds
on rows in sugar beet fields. The images captured with
the CCD camera on the spraying robot were processed
using image processing algorithms in Matlab software.
Weeds in the row were detected by using Multilayer
Perceptron Algorithm with the data which are obtained
from the images and spraying liquid was applied
on them. Kiani et al. (2010) pointed out at the fact
that the use of chemicals for weed control in wheat
fields caused environmental pollution. Due to this
pollution, they stated that alternative methods such as
image processing could be used for detecting weeds.
Accordingly, Multilayer Perceptron Algorithm was
used in the study in order to classify and analyze the
Research for Rural Development 2016, volume 1
CLASSIFICATION OF DIFFERENT FOREST TYPES
WITH MACHINE LEARNING ALGORITHMS
properties of energy, entropy, contrast, homogeneity
and inertia. Babalik et al. (2010) used artificial
neural networks and image processing techniques to
determine the vitreousness of hard wheat, they used
artificial neural networks to classify the vitreous
and non-vitreous kinds of Type-1252 wheat. In this
study, the classification success rates of self-regulated
mapping (SRM) and multilayer perceptron (MP) were
examined. Sabanci et al. (2012) classified potatoes in
terms of their size with the help of image processing
techniques and artificial neural network. Before the
classification process, potatoes with surface defects
and deformities were detected using Otsu method
and morphological processes, and they were excluded
from the classification. Then potatoes were classified
based on their sizes. For this process, the images of
small, medium and big sized potatoes were captured
and the system was trained using multilayer artificial
neural networks. Using image processing techniques
and artificial neural networks, the classification
success rates of potatoes were analyzed. Karthikeyan
et al. (2015) obtained the dataset values (from the
UCI database) of hepatitis disease that occurs on
the liver and applied J48, Naïve Bayes, Multilayer
Perceptron, random forest classification algorithms
using these datasets. As a result of the study, the
highest percentage rate in the classification of
hepatitis patients based on sick cells was obtained
using the Naïve Bayes algorithm. Polat and Gunes
(2009) offered a genuine hybrid classification system
that is based on the C4.5 decision tree classifier and
a one-against-all approach to classify the multi-class
problems including image segmentation, dermatology
and lymphography datasets obtained from the UCI
MRL database. Sabanci et al. (2015) used the EEG
eye state dataset obtained from the UCI machine
learning repository database. 14 continuous EEG
measurements constitute the basics of the dataset.
The duration of the measurement was 117 seconds
(each measurement had 14980 samples). They used
Multilayer Perceptron Neural Network Models
and k-Nearest Neighbor Algorithm to calculate the
classification success rate. The classification success
rates were measured for varying number of neurons in
the hidden layer of the Multilayer Perceptron Neural
Networks model. The highest classification success
rate was achieved when the number of neurons in the
hidden layer was 7. And the success rate was 56.45%.
The classification success rates were measured
using k-Nearest neighbor algorithm for varying
neighborhood values. The highest classification
success rate was achieved using kNN algorithm. In
k-Nearest neighbor model, the success rate regarding
3 nearest neighbors was measured as 84.05%. Yu et
al. (2015) classified the forest trees in Zijin Mountains
National Forest Park in Nanjing, China. The data is of
Kadir Sabanci, M.Fahri Ünlerşen, Kemal Polat
the year 2011. Three types of band combinations were
compared based on the accuracy of the classification.
Using the obtained optimal band combination,
decision tree classifier, neural networks and support
vector machine classification methods were
compared. The best result was obtained using 8-bant
combination for decision tree classification, and the
success rate was 87.10%. It was determined that
artificial neural networks produced the worst results
with the success rate of 73.85%. Aguiar et al. (2010)
identified forages in the state of Mato Grosso do Sul
in Brazil and their varying decomposition levels. In
order to obtain the plant cover index and fractional
images, MODIS duration series were used. Using
ripple technique at various decomposition levels, the
input parameter required for WEKA J48 classifier
was obtained. This way, forages were successfully
selected from Cerrado. The segregation between
different forages caused lower performance; the best
results were obtained for forages that include common
plants. Yang et al. (2014) classified trees in Boreal
forests in Canada. Using LiDAR, RapidEye and the
combination of these two data along with the support
vector machine classification method, the success
rates were compared. The data they used composed
of six components. These are digital elevation model,
slope, red-edge NDVI, red-edge, canopy height and
near infrared bands of RapidEye data. The best result
was obtained using the combination of LiDAR and
RapidEye data.
In this paper, the forest type mapping including
three tree types have been automatically classified
based on machine learning algorithms including
k-NN, Multilayer Perceptron, J48, Bayes Net, Naïve
Bayes and K-Star classification methods using spectral
features belonging to these tree types.
Materials and Methods
Dataset
In this study, Advanced Spaceborne Thermal
Emission and Reflection Raidometer (ASTER)
satellite images (15m resolution) in a forestland
of approximately 13 x 12 km in Ibaraki Prefecture,
Japan were used. In this area, there were mainly
Cryptomeria japonica (Sugi) trees, Chamaecyparis
obtuse (Hinoki) trees, mixed broadleaf, angiosperm
natural trees and also a few non-forest structures (such
as buildings, roads and cultivated areas) (Johnson,
Tateishi, & Xie, 2012). The orthorectificated ASTER
images were obtained at three different dates in order
to determine the coniferous and broadleaf tree types.
Each pixel identifies a distance of 15m. The images
were obtained at green (0.52 – 0.60 µm), red (0.63 –
0.69 µm) and near infrared (NIR) (0.76 – 0.86 µm)
bands (as a total of nine bands) (Johnson, Tateishi, &
Xie, 2012). The data obtained from the UCI Forest
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CLASSIFICATION OF DIFFERENT FOREST TYPES
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Kadir Sabanci, M.Fahri Ünlerşen, Kemal Polat
type mapping data set are composed of two parts as
training and test. There is a total of 524 data. The 38%
of the data is for training and 62% is for test. Each
data consists of 27 attributes. Each data is classified
as Sugi, Hinoki, mixed broadleaf and others (UCI,
2016). Using the data obtained for each channel by
processing the orthorectificated ASTER images
using the inverse distance weighting (IDW) method,
a map for Sugi and Hinoki type trees was created.
The nearest 15 neighbor pixels were used for IDW
process. Using the training data, the average spectral
values of Sugi and Hinoki types at each band were
obtained. For Sugi, pred_minus_obs_S was obtained
by subtracting the values obtained with IDW from
average values. For Hinoki, pred_minus_obs_H was
obtained by subtracting the values obtained with
IDW from average values. Therefore, a 27 attribute
set composed of 9 original values, 9 pred_minus_obs
values and 9 pred_minus_obs_H values was obtained
(Johnson, Tateishi, & Xie, 2012).
Software-WEKA
Developed by Waikato University in New Zealand,
WEKA is an open-source data mining software with
a functional graphical interface which incorporates
machine learning algorithms (Witten, Frank, & Hall,
2011.). WEKA includes various data pre-processing,
classification, regression, clustering, association rules,
and visualization tools. The algorithms can be applied
on the data cluster either directly or by calling via Java
code (Patterson et al., 2008; Hall et al., 2009). They
are also suitable for developing new machine learning
algorithms.
Machine learning algorithms
K-Nearest Neighbor Algorithm: The k-NN is a
supervised learning algorithm that solves classification
problems. Classification is the examination of the
attributes of an image and the designation of this
image to a predefined class. The important point is
the determination of the features of each category in
advance (Wang, Neskovic, & Cooper, 2007). According
to the kNN algorithm used in the classification, based
on the attributes drawn from the classification stage,
the distance of the new individual that is wanted to be
classified to all previous individuals is considered and
the nearest k class is used. As a result of this process,
test data belongs to the k-nearest neighbor category
that has more members in a certain class. The most
important optimization problems in the kNN method
are the identification of the number of neighbors and
the method of distance calculation algorithm. In the
study, the identification of the optimum k number
is performed with experiments, and the Euclidean
Distance Calculations method is used as a distance
calculation method.
256
Euclidean calculation method (Zhou, Li, & Xia,
2009):
xi and xj are two different points, and we need
distance calculation process in between.
Multilayer Perceptron: It is a feed forward type
artificial neural network model which maps input
sets onto appropriate output sets. A multilayer
perceptron (MLP) is composed of multiple layers of
nodes where each layer is connected to the next. Each
node is a processing element or a neuron that has a
nonlinear activation function except the input nodes.
It uses a supervised learning technique named back
propagation and it is used for training the network.
The alteration of the standard linear perceptron, MLP
is capable of distinguishing data which are not linearly
separable (Hall et al., 2009).
J48: It is a widely used machine learning algorithm
that is based on J.R. Quilan C4.5 algorithm. Data that
will be examined will belong to the categorical type,
so continuous data will not be examined at this step.
However, the algorithm will leave room for adaptation
in a way to include this capability (Hall et al., 2009;
Arora, 2012).
Bayes Net: Bayes Net is a probabilistic graphical
model and a statistical model representing a group
of random variables in addition to their conditional
dependencies through a directed acyclic graph.
For instance, a Bayesian network can represent
the probabilistic relations between diseases and
symptoms. When the symptoms are given, the network
can calculate the probabilities of the existence of
various diseases (Hall et al., 2009).
Naive Bayes: In a learning problem, Naïve Bayes
classifiers have a high degree of scalability and they
entail a number of parameters that are linear with
the number of variables (predictors/features). The
maximum-likelihood training could be performed by
examining a closed-form expression that takes linear
time instead of by expensive iterative approximation
unlike many other types of classifiers (Hall et al.,
2009).
KStar: K* or K-Star is a classifier based on
instance. A test instance class depends on the training
instances that are similar to it, and it is determined by
various similarity functions. The point it is different
from other instance-based learners is that it uses a
distance function that is based on entropy (Cleary &
Trigg, 1995).
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WITH MACHINE LEARNING ALGORITHMS
Results and Discussion
In the study, WEKA software was used in order
to classify 3 different forest types (Sugi, Hinoki,
mixed broadleaf). Using the kNN algorithm, the
classification success rates of different forest types
were obtained for different k-neighbor values. Also,
root mean square error (RMSE) and mean absolute
error (MAE) values were obtained. The classification
success rates obtained with kNN algorithm, and MAE
and RMSE values can be seen in Table 1. The diagram
demonstrating the changes in MAE and RMSE error
values based on the number of neighbors in the
classification performed with the kNN algorithm is
shown in Figure 1.
The data in the same dataset were processed using
the multilayer perceptron model, and the classification
success rates of different forest types were obtained.
The classification success rates of different number of
neurons in the hidden layer, and MAE and RMSE error
Kadir Sabanci, M.Fahri Ünlerşen, Kemal Polat
values were obtained. In the multilayer perceptron
model, the training was performed by taking the
learning rate value as 0.3, momentum value as 0.2 and
iteration number as 500. The classification success
rates, and MAE and RMSE values obtained using the
multilayer perceptron model can be seen in Table 2.
The diagram demonstrating the changes in MAE and
RMSE error values based on the number of neighbors
in the hidden layer is demonstrated in Figure 2.
Then the same data was processed using J48, Naïve
Bayes, Bayes Net, KStar machine learning algorithms
and classification success rates and MAE and RMSE
error values of different tree types in the forest were
obtained. The success and error rates obtained using
6 different classification algorithms (Multilayer
Perceptron, kNN, J48, Naïve Bayes, Bayes Net, KStar)
can be seen in Table 3. The diagram demonstrating
the error values obtained based on different machine
learning algorithms can be seen in Figure 3.
Table 1
The Success Rate and Error Values Obtained by using kNN Classifier
Neighborliness Number (k)
Classification accuracy (%)
MAE
RMSE
1
83.3652
0.0856
0.2872
2
83.3652
0.0839
0.2412
3
88.1453
0.0834
0.2271
4
87.9541
0.0836
0.2198
5
89.1013
0.0877
0.2169
6
88.7189
0.0903
0.2181
7
88.7189
0.0908
0.2158
8
88.1453
0.0922
0.2139
9
88.9101
0.0941
0.2145
10
88.3365
0.0954
0.2141
Figure 1. Variation of error rate based on the number of neighborhood.
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CLASSIFICATION OF DIFFERENT FOREST TYPES
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Kadir Sabanci, M.Fahri Ünlerşen, Kemal Polat
Success Rate Obtained By Using Multilayer Perceptron Classifier Error Values
Table 2
The number of neurons in the
hidden layer
Classification accuracy (%)
MAE
RMSE
5
88.9101
0.0611
0.2157
10
90.0574
0.0585
0.2068
15
88.7189
0.0621
0.2127
20
90.0574
0.0563
0.2007
25
89.4837
0.059
0.2091
30
89.6750
0.0569
0.2094
40
90.0574
0.0563
0.2054
50
89.6750
0.0566
0.2062
60
89.6750
0.0549
0.2056
70
89.8662
0.0543
0.2027
80
88.9101
0.0601
0.2146
90
90.4398
0.0572
0.2078
100
89.8662
0.0557
0.204
Figure 2. Variation of error rate based on the number of neurons in hidden layer.
Table 3
Success Rate Obtained By Using Various Machine Learning Algorithms
Machine learning algorithms
Classification accuracy (%)
MAE
RMSE
Multilayer Perceptron
90.4398
0.0572
0.2078
kNN
89.1013
0.0877
0.2169
258
J48
86.0421
0.0810
0.2543
Naive Bayes
85.6597
0.0708
0.2562
Bayes Net
85.4685
0.0729
0.2593
KStar
81.4532
0.0933
0.2933
Research for Rural Development 2016, volume 1
CLASSIFICATION OF DIFFERENT FOREST TYPES
WITH MACHINE LEARNING ALGORITHMS
Kadir Sabanci, M.Fahri Ünlerşen, Kemal Polat
Figure 3. Variation of error rate based on the machine learning algorithms.
Conclusions
In this study, three different forest types in Japan
(Sugi, Hinoki, mixed broadleaf) were classified using
machine learning algorithms (Multilayer Perceptron,
kNN, J48, Naïve Bayes, Bayes Net, KStar). The
classification success and error values of machine
learning algorithms were calculated. It was observed
that the success rate was higher for the classifications
performed using the Multilayer Perceptron Algorithm.
The highest classification success rate was achieved
when the number of neurons in the hidden layer was
80 and the success rate was 90.4398%. The MAE error
value was 0.0572 and the RMSE error value was 0.2078
for the number of neurons in the hidden layer. For the
classification success rates obtained using K-Nearest
Neighbor Algorithm, the highest classification success
rate was achieved for 5 neighborhood values, and it
was 89.1013%. For this neighborhood value, the MAE
error value was 0.0877 and the RMSE error value was
0.2169. The success rates obtained using J48, Naïve
Bayes, Bayes Net and KStar classification algorithms
were found as 86.0421%, 85.6597%, 85.4685% and
81.4532% respectively.
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